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A new technique from Zhejiang University and Alibaba Group gives large language model (LLM) agents a dynamic memory, making them more efficient and effective at complex tasks. The technique, called Memp, provides agents with a “procedural memory” that is continuously updated as they gain experience, much like how humans learn from practice.
Memp creates a lifelong learning framework where agents don’t have to start from scratch for every new task. Instead, they become progressively better and more efficient as they encounter new situations in real-world environments, a key requirement for reliable enterprise automation.
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The case for procedural memory in AI agents
